Methods and systems for radiotherapy treatment planning

ABSTRACT

Example methods for radiotherapy treatment planning are provided. One example method may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients; and processing the training data to determine, from each of the multiple treatment plans, at least one of the following: first data associated with a particular past patient or a radiotherapy system for delivering radiotherapy treatment to the particular past patient, second data associated with treatment planning trade-off selected for the particular past patient and third data associated with radiation dose for delivery to the particular past patient. The method may also comprise: based on at least one of the first data, the second data and the third data, identifying one or more sub-optimal characteristics associated with the training data, obtaining improved training data and generating a dose estimation model based on the improved training data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of U.S. patent application,Ser. No. 15/377,962, filed Dec. 13, 2016 (Attorney Docket No.124-0025-US-REG). The U.S. patent application Ser. No. 15/377,962,including any appendices or attachments thereof, is incorporated byreference herein in its entirety.

BACKGROUND

Unless otherwise indicated herein, the approaches described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Radiotherapy is an important part of a treatment for reducing oreliminating unwanted tumors from patients. Unfortunately, appliedradiation does not inherently discriminate between an unwanted tumor andany proximal healthy structures such as organs, etc. This necessitatescareful administration to restrict the radiation to the tumor (i.e.,target). Ideally, the goal is to deliver a lethal or curative radiationdose to the tumor, while maintaining an acceptable dose level in theproximal healthy structures. However, in practice, there are variouschallenges associated with radiotherapy treatment planning to deliverradiation doses that achieve this goal.

SUMMARY

In accordance with some embodiments of the present disclosure, examplemethods and systems for generating a dose estimation model forradiotherapy treatment planning are provided.

In one embodiment, the method may comprise: obtaining training data thatincludes multiple treatment plans and processing the training data todetermine, from each of the multiple treatment plans, first dataassociated with patient geometry, second data associated with treatmentplanning trade-off and third data associated with radiation dose. Themethod may further comprise: using the first data, second data and thirddata from the multiple treatment plans, training the dose estimationmodel to estimate a relationship that transforms the first data andsecond data to the third data.

In accordance with some embodiments of the present disclosure, examplemethods and systems for radiotherapy treatment planning are provided. Inone embodiment, the method may include obtaining training data thatincludes multiple treatment plans associated with respective multiplepast patients; and processing the training data to determine, from eachof the multiple treatment plans, at least one of the following: firstdata associated with a particular past patient or a radiotherapy systemfor delivering radiotherapy treatment to the particular past patient,second data associated with treatment planning trade-off selected forthe particular past patient and third data associated with radiationdose for delivery to the particular past patient. The method may alsocomprise: based on at least one of the first data, the second data andthe third data, identifying one or more sub-optimal characteristicsassociated with the training data; obtaining improved training data thatis generated to reduce or eliminate the identified one or moresub-optimal characteristics associated with the training data; andgenerating a dose estimation model based on the improved training data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an example process flow forradiotherapy treatment;

FIG. 2 is a flowchart of an example process for a computer system togenerate a dose estimation model for radiotherapy treatment planning;

FIG. 3 is a schematic diagram illustrating an example process for acomputer system to train a dose estimation model for radiotherapytreatment planning;

FIG. 4 is a flowchart of an example process for a computer system toautomatically generate a treatment plan based on the dose estimationmodel trained according to the example process in FIG. 3;

FIG. 5 is a schematic diagram of an example radiotherapy treatmentsystem 500 for treatment delivery according to a treatment plangenerated according to the example process in FIG. 4;

FIG. 6 is a flowchart of an example process for a computer system toperform radiotherapy treatment planning;

FIG. 7 is a schematic diagram illustrating an example process for acomputer system to identify a first example sub-optimal characteristicin the form of gap(s) in training data;

FIG. 8 is a schematic diagram illustrating an example process a computersystem to identify a second example sub-optimal characteristic in theform of treatment plan(s) that require re-planning or include treatmentplanning trade-off in training data; and

FIG. 9 is a schematic diagram of an example computer system forradiotherapy treatment planning and an example radiotherapy treatmentsystem for treatment delivery.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

FIG. 1 is a schematic diagram illustrating example process flow 100 forradiotherapy treatment. Example process 100 may include one or moreoperations, functions, or actions illustrated by one or more blocks,such as 110 to 170. The various blocks may be combined into fewerblocks, divided into additional blocks, and/or eliminated based upon thedesired implementation. In the example in FIG. 1, radiotherapy treatmentgenerally includes various stages, such as image data acquisitionperformed by an imaging system (see 110-120); image processing by animage processing system (see 130-140); treatment planning by a treatmentplanning system (see 150-160); and treatment delivery by a radiotherapytreatment system (see 170).

As will be further described below, treatment planning (see 150 inFIG. 1) may be improved according to examples of the present disclosureto facilitate better treatment delivery. For example, given the oftenclose proximity between tumors and proximal healthy structures (e.g.,organs, etc.), any improvement in treatment planning has the potentialof improving treatment outcome, such as increasing the tumor controlprobability and/or reducing the likelihood of minor through severehealth complications or death due to radiation overdose in the healthystructures.

At 110 in FIG. 1, image data acquisition is performed using an imagingsystem to capture image data 120 of a patient's anatomy requiringradiotherapy treatment. Any suitable imaging modality or modalities maybe used by the imaging system, such as computed tomography (CT),positron emission tomography (PET), magnetic resonance imaging (MRI),single photon emission computed tomography (SPECT), etc. When CT or MRIis used, image data 120 may include a series of two-dimensional (2D)images or slices, each representing a cross-sectional view of thepatient's anatomy.

At 130 in FIG. 1, image processing is performed to process image data120 using an image processing system, such as to reconstruct athree-dimensional (3D) volume of the patient's anatomy from image data120. The 3D volume is known as “treatment volume” 140 that will besubjected to radiation. Treatment volume 140 may be divided intomultiple smaller volume-pixels (voxels), each voxel representing a 3Delement associated with location (i, j, k) within treatment volume 140.At this stage, information that is useful for subsequent treatmentplanning 150 may be extracted from treatment volume 140, such as datarelating to the contour, shape, size and location of patient's anatomy144, target 146 and any organ-at-risk (OAR) 148.

In practice, target 146 may represent a malignant tumor (e.g., prostatetumor, etc.) requiring radiotherapy treatment, and OAR 148 a proximalhealthy structure (e.g., rectum, bladder, etc.) that might be adverselyaffected by the treatment. Target 146 is also known as a planning targetvolume (PTV), and treatment volume 140 as an irradiated volume. Althoughan example is shown in FIG. 1, treatment volume 140 may include multipletargets 146 and OARs 148 with complex shapes and sizes in practice.Further, although shown as having a regular shape (e.g., cube), voxel142 may have any suitable shape (e.g., non-regular).

At 150 in FIG. 1, treatment planning is performed using a treatmentplanning system to determine treatment plan 160. For example in FIG. 1,treatment plan 160 specifies radiation dose for target 146 (denoted“D_(TAR)” at 162) and radiation dose for OAR 148 (denoted “D_(OAR)” at164).

At 170 in FIG. 1, treatment delivery 170 is performed using aradiotherapy treatment system to deliver radiation to the patientaccording to treatment plan 160. In practice, treatment planning may beperformed based on goal doses prescribed by a clinician (e.g.,oncologist, dosimetrist, treatment planner, etc.), such as based on theclinician's experience, the type and extent of the tumor at target 146,patient geometry and condition, etc.

An important aspect of treatment planning 150 is estimating the level ofradiation dose to be applied to the patient. In practice,knowledge-based treatment planning may be used, in which achievabledoses for target 146 and OAR 148 are estimated based on existingclinical knowledge. This involves training a “dose estimation model”(also known as “dose prediction model”) using a set of treatment plans(also known as “training data”) previously devised for past patients.Ideally, the training data should be of high quality, and sufficientlysimilar to a treatment being planned for a new patient (e.g., similartreatment area, etc.). Once trained, the dose estimation model may beused to automatically determine treatment plan 160 for the new patient.

Conventionally, a dose estimation model contains rules to transformpatient geometry data (i.e., known prior to optimization and called“independent data”) into dosimetrical data (i.e., known after theoptimization and called “dependent data”). When used with anoptimization algorithm, estimates produced by the dose estimation modelmay be optimized according to optimization objectives set by theclinician to produce complete treatment plan 160. However, in practice,it is not always clear what “optimality” refers to.

For example, in intensity-modulated radiation therapy (IMRT), OAR doselevel (see D_(OAR) 164) may be reduced by accepting a looser conformitywith target dose level (see D_(TAR) 162). In case of multiple OARs, thedose level for one OAR may be reduced by accepting a higher dose toanother OAR. The resulting “optimal” dose distribution thereforereflects, to a certain extent, the preferences of the clinician, whichin turn might reflect at least partly the additional knowledge theclinician has from the patient. Also, the objective set by the clinicianmight cause some arbitrariness to the “optimal” dose distribution.

For the above reasons, the training data for training a dose estimationmodel may have additional variation due to somewhat different criteriathat cannot be explained by considering the patient geometry data alone.This may cause several problems in practice. In one example, theadditional variation might increase unexplained variance of the doseestimation model, thereby making dose estimations less accurate. Inanother example, treatment plans that have significantly differenttrade-off (e.g., between OAR dose level and target coverage) areconsidered outliers, and additional work is required to remove suchoutliers from the training data.

Ideally, a clinician should be able to recognize treatment plans withtrade-offs and exclude them from the training data (usually performedmanually). However, it is not always straightforward to identify suchtreatment plans. For example, treatment plans that appear to have thesame goals might rely on different trade-offs between objectives. Toidentify such treatment plans, a comparison between a dose specified ina treatment plan used for training, and a dose estimated by theresulting dose estimation model is required. For example, the treatmentplan may specify a significantly lower dose than the dose estimationmodel is predicting in one OAR, but a significantly higher dose than themodel is predicting in another OAR. This suggests that the treatmentplan relies on a non-canonical balance between the sparing of these twoOARs, and should be removed.

Even when the above treatment plans are successfully identified andexcluded from the training data, their exclusion might present anotherproblem. In particular, once the treatment plans are removed, the scopeof the resulting dose estimation model would be reduced because itcannot be applied in situations with treatment planning trade-offs. Ifthe clinician prefers improved target dose conformity in certain cases,this will not be understood by the dose estimation model.

Dose Estimation Model with Treatment Planning Trade-Off

According to examples of the present disclosure, radiotherapy treatmentplanning may be improved using a dose estimation model that takes intoaccount treatment planning trade-off. Instead of necessitating theremoval of certain treatment plans that are considered to be outliers,the training data may include such outliers to train a more flexibledose estimation model. This way, for example, the training data may beused more freely and the trade-off between various objectives may beconsidered using the same dose estimation model.

In more detail, FIG. 2 is a flowchart of example process 200 for acomputer system to generate a dose estimation model for radiotherapytreatment planning. Example process 200 may include one or moreoperations, functions, or actions illustrated by one or more blocks,such as 210 to 250. The various blocks may be combined into fewerblocks, divided into additional blocks, and/or eliminated based upon thedesired implementation. Example process 200 may be implemented using anysuitable computer system, an example of which will be discussed usingFIG. 9.

At 210 in FIG. 2, training data that includes multiple treatment plansassociated with respective multiple past patients are obtained. Here,the term “obtain” may refer generally to retrieving the training datafrom any suitable storage (e.g., database of historical treatment plans)accessible by the computer system, receiving the training data fromanother source via any suitable communication link, etc.

At 220 in FIG. 2, the training data is processed to determine, from eachof the multiple treatment plans, first data that includes one or morefeatures associated with a particular past patient. For example, thefirst data may include one or more features associated with patientgeometry, such as target volume, OAR volume, relative overlap volume(i.e., fraction of target volume overlapping with OAR volume), relativeout-of-field volume (i.e., fraction of target or OAR volume outside ofthe treatment field), distance to target (DTH) values that expresses thedistance from a region or structure such as OAR 148 from target 146,etc. Any other suitable first data may be used in practice.

At 230 in FIG. 2, the training data is processed to determine, from eachof the multiple treatment plans, second data associated with treatmentplanning trade-off selected for the particular past patient. As usedherein, the term “trade-off” may refer generally to a balance betweentwo treatment objectives (e.g., between “first objective” and “secondobjective”) during radiotherapy treatment planning, which may be bothdesirable but competing with each other.

The second data may include one or more dosimetrical features associatedwith the first objective or second objective, such as OAR dose level(e.g., mean dose, median dose, maximum dose, minimum dose, etc.),relative target volume with a particular prescribed dose or higher,desired normalization volume for 98% dose level (or any other suitablelevel), etc. Alternatively or additionally, the second data may includeone or more non-dosimetrical features associated with the firstobjective or second objective, such as Monitor Unit (MU), deviation ofMU from an average value, treatment time, machine-related feature(s) ofa radiotherapy treatment system (see FIG. 6), etc. As will be discussedfurther using FIG. 3, it should be understood that the trade-off may beone-to-one, one-to-many, or many-to-many.

At 240 in FIG. 2, the training data is processed to determine, from eachof the multiple treatment plans, third data associated with radiationdose for delivery to the particular past patient. The third data mayinclude one or more features associated with radiation dose, such 3Ddose distribution, dose-volume histograms (DVH), etc. In general, a 3Dradiation dose distribution defines the magnitude of radiation at eachvoxel representing target 146 or OAR 148. 3D dose distributions may besummarized using dose-volume histograms (DVH) in a 2D format. Radiationdose may be measured in Gray (Gy), which represents the absorption ofone joule of radiation energy in one kilogram of matter.

At 250 in FIG. 2, a dose estimation model is generated by training,based on the first data, second data and third data from the multipletreatment plans, the dose estimation model to estimate a relationshipthat transforms the first data and second data to the third data. In oneexample, the generated dose estimation model expresses radiation dose(i.e., dependent third data) as a function of patient geometry andtreatment planning trade-off (i.e., independent first and second data).As will be described further using FIG. 4, once trained using themultiple treatment plans devised for different patients, the doseestimation model may be used to predict or estimate radiation dose for aparticular patient.

In the following, various examples will be discussed using FIG. 3 toFIG. 9. In particular, example dose estimation model training will bediscussed using FIG. 3, example treatment planning using FIG. 4 and FIG.5, and example computer system and radiotherapy system using FIG. 9.

Dose Estimation Model Training

FIG. 3 is a schematic diagram illustrating example process 300 for acomputer system to train a dose estimation model for radiotherapytreatment planning. Example process 300 may include one or moreoperations, functions, actions or data items illustrated by one or moreblocks, such as 310 to 365. The various blocks may be combined intofewer blocks, divided into additional blocks, and/or eliminated basedupon the desired implementation. Similar to the example in FIG. 2,example process 200 may be implemented using any suitable computersystem, an example of which will be discussed using FIG. 9.

Example process 300 may be implemented to train a dose estimation modelfor any suitable radiotherapy treatment planning, such as relating tocancer treatment, etc. For example, in relation to lung cancer, target146 represents cancerous lung tissue, and OAR 148 may be proximalhealthy lung tissue, esophagus, heart, etc. In relation to prostatecancer, target 146 represents a patient's prostate, and OAR 148 aproximal healthy structure such as rectum, bladder, etc. In thefollowing, an example will be described in relation to prostate cancer.

Referring first to 305 in FIG. 3, training data that includes Ntreatment plans associated with respective multiple past patients isobtained, such as by retrieving from a database of past treatment plans,receiving from another computer system via a communication link, etc.The training data may be obtained based on their relevance to aparticular treatment area, such as prostate in the example below. Aparticular treatment plan in training data 305 may be denoted as thei^(th) treatment plan, where i=1, . . . , N.

At 310, 320 and 330 in FIG. 3, training data 305 is processed todetermine various data (see “first data” 315, “second data” 325, “thirddata” 335) required to train the dose estimation model. Here, the term“process” or “processing” may include any suitable data processingoperation(s), such as data analysis, feature extraction, calculation,derivation, transformation, any combination thereof, etc. First data 315and second data 325 represent the input or “independent” variables ofthe dose estimation model, while third data 335 represents the output or“dependent” variables.

Prior to the processing at 310, 320 and 330 in FIG. 3, a clinician mayselect specific parameter(s) or feature(s) of first data 315, seconddata 325 and third data 335 to be determined from training data 305. Forexample, the feature(s) may be selected or entered via a graphical userinterface (GUI) provided by the computer system, etc. The specificfeature(s) may depend on the clinician's experience and knowledge, typeof radiotherapy treatment required, beam configuration (e.g., energy,collimator size and orientations), etc. The clinician may also rely onany expertise or knowledge relating to the biological effect ofradiation on target 146 and/or OAR 148, such as based on tumor controlprobability, normal tissue complication probability, etc. The tumorcontrol probability is the probability of eradicating all tumor cells asa function of dose. The normal tissue complication probability is theprobability of, as a function of dose, inducing some particularcomplication (a collective word for describing a variety of conditionssuch as nausea, vomiting, etc.) in a normal organ.

At 310 in FIG. 3, training data 305 is processed to determine first data315 that includes K≥1 feature(s) associated with patient anatomy. Firstdata 315 may be represented using a N×K matrix denoted as X, andfeature(s) from the i^(th) treatment plan may be represented asX_(i)=(X_(i1), . . . , X_(iK)), i=1, . . . , N. In practice, first data315 may include any suitable patient geometrical or anatomicalfeature(s) that can be extracted or derived from training data 305, suchas target volume; OAR volume; relative volume; relative out-of-fieldvolume, etc. Distance to target histograms (DTHs) that measure thedistance from particular target 146 may also be derived from trainingdata 305.

At 320 in FIG. 3, training data 305 is processed to determine seconddata 325 that includes L≥1 feature(s) associated with treatment planningtrade-off. Second data 325 may be represented using a N x L matrixdenoted as Y. and feature(s) from the i^(th) treatment plan may berepresented as Y_(i)=(Y_(i1), . . . , Y_(iL)), i=1, . . . , N. Forexample, in the case of L=1, Y_(i) is a 1D vector with a singular valueY_(i). In practice, second data 325 may be associated with a treatmentplanning trade-off between a first objective and a second objective. Thefirst objective or second objective may be parameterized using anysuitable dosimetrical and/or non-dosimetrical feature(s) that can beextracted or derived from training data 305.

According to examples of the present disclosure, any suitable treatmentplanning trade-off between a first objective and a second objective maybe considered. The trade-off may be one-to-one, one-to-many, ormany-to-many. Some examples will be discussed below.

(a) In a first example, a one-to-one trade-off may be between a firstobjective associated with OAR 148 (e.g., rectum) and a second objectiveassociated with target 146 (e.g., prostate). In this case, the firstobjective may relate to dose sparing of OAR 148, and the secondobjective may relate to better target coverage or better target doseconformity. In this example, the second data may include one or moredosimetrical features, such as relative target volume with a particularprescribed dose or higher, desired normalization volume for 98% doselevel (or any other suitable level), etc. Dose sparing of OAR 148 may becharacterized using OAR dose level, such as mean dose, median dose,maximum dose, minimum dose, etc.

(b) In a second example, a one-to-one trade-off may be between a firstobjective associated with a first OAR (e.g., rectum) and a secondobjective associated with a second OAR (e.g., bladder) to consider dosesparing of multiple OARs. In this example, the second data may includeone or more dosimetrical features, such as a ratio between a mean doseof the first OAR and a mean dose of the second OAR; a ratio between amaximum dose of the first OAR and mean dose of the second OAR; etc.

(c) In a third example, a one-to-many trade-off may be between a firstobjective associated with target 146 (e.g., prostate) and a secondobjective associated with multiple OARs (e.g., rectum and bladder). Inthis case, the first objective may relate to better target coverage orbetter target dose conformity, and the second objective may relate tocombined dose sparing of the multiple OARs. Similar to the firstexample, the first objective may be represented using a relative targetvolume with a particular prescribed dose or higher, desirednormalization volume for 98% dose level (or any other suitable level),etc. Combined dose sparing may be represented using a combined doselevel (e.g., mean dose) of the multiple OARs.

(d) In a fourth example, a one-to-one trade-off may be between a firstobjective associated with a first feature that is non-dosimetrical, anda second objective associated with a second feature (dosimetrical ornon-dosimetrical). For example, the first feature may be MU, whichmeasures a machine output from an accelerator of the radiotherapysystem, such as a linear accelerator (LINAC), etc. In this example, thesecond data may include a deviation of MU from an average value (e.g.,“normal MU level”=500). In practice, MUs may be in the order of fewhundred to a thousand when calibrated in a traditional way, but inprinciple this is a parameter with free scaling. A higher MU may be usedto improve one OAR, while keeping the dose in others roughly the same.The second data may indicate ‘more MU(s) than normal’ (i.e., positivedeviation) or ‘less MU(s) than normal’ (i.e., negative deviation).

(e) In a fifth example, a one-to-many trade-off may be between a firstobjective associated with a first feature that is non-dosimetrical, anda second objective associated with multiple second features(dosimetrical, non-dosimetrical, or a combination of both). Using MU asan example, a higher MU may be utilized to improve the overall treatmentplan. In this case, similar to the fourth example, the second data mayinclude the deviation of a Monitor Unit (MU) from an average value(e.g., MU=500). Although MU has been used as an example, any othersuitable non-dosimetrical feature may be considered, such as treatmenttime, etc.

(f) An extension to the above examples is a many-to-many trade-offbetween a first objective associated with a first group of features anda second objective associated with a second group of features. In thiscase, the second data may include a combined value (e.g., mean dose)representing the first group or second group. The features in each groupmay be dosimetrical, non-dosimetrical, or a combination of both.

At 330 in FIG. 3, training data 305 is processed to determine third data335 that includes M ≥1 feature(s) associated with radiation dose to bedelivered. Third data 335 may be represented using a N×M matrix denotedas Z. Feature(s) from the i^(th) treatment plan may be represented usingZ_(i)=(Z_(i1), . . . , Z_(iM)), i=1, . . . , N. For example, in the caseof M=1, Z_(i) is a 1D vector with a singular value Z_(i). In practice,radiation dose may be specified using dose distribution, DVH, etc.

At 340 in FIG. 3, dose estimation model 345 is trained using (X, Y, Z)to estimate a relationship that transforms independent data (X, Y) todependent data Z. For example, the relationship or interdependency maybe expressed using any suitable function f( ) as follows:

Z=f(X, Y).

Any suitable algorithm may be used to estimate function f( ), such asregression algorithm (e.g., stepwise multiple regression, linearregression, polynomial regression, etc.) to estimate a set ofcoefficients that transform (X, Y) to Z. It should be understood thatany additional and/or alternative algorithm may be used to train thedose estimation model, such as principal component analysis (PCA)algorithm, classification algorithm, clustering algorithm, machinelearning algorithm (e.g., supervised learning, unsupervised learning),etc.

For simplicity, consider an example with K=2, L=1 and M=1 in FIG. 3. Inthis case, first data 315, second data 325 and third data 335 from thei^(th) treatment plan may be expressed as (X_(i), Y_(i), Z_(i))=(X_(i2),X_(i2), Y_(i), Z_(i)), where i =1, . . . , N. In relation to prostatecancer treatment planning, target 146 may be a patient's prostate andOAR 148 the rectum. For example, dose estimation model 345 may bedesigned to take into account the treatment planning trade-off betweenhigher mean dose in rectum (i.e., first objective) and better targetcoverage (i.e., second objective). In this case, X_(i1)=rectum volume;X_(i2)=closest distance between rectum and prostate; Y_(i)=relativetarget volume covered by a dose prescription level and Z_(i)=mean doseon rectum.

Function f( ) may be presented as a multiplication of combined X and Y(see 360 in FIG. 3) with a matrix of coefficients (see 355 in FIG. 3).In the example in FIG. 3, ε_(i) represents the difference (see 356 inFIG. 3) between Z_(i) (see 350 in FIG. 3) derived from training data 305and f(X_(i1), X_(i2), Y_(i)) estimated by the model. For example,assuming f( ) is linear, linear regression may be used to estimate thefollowing dose estimation model:

Z _(i)=∝+β₁ X _(i1)+β₂X_(i2) +γY _(i)+ε_(i).

In the above equation, β₁, β₂ and γ are known as the coefficientsassociated with respective independent features X_(i1), X_(i2) andY_(i); and ∝ is also known as the intercept. In general, ∝, β₁, β₂ and γare dimensionless units. To estimate f( ), values of (α, β₁, β₂, γ) thatbest fit training data 305 are calculated, such as by minimizing theleast-squared errors ε²=(f(X,Y)−Z)².

In practice, training dose estimation model 345 may involve estimating arelationship Ŷ=h(X) between first data 315 and second data 325 beforeadding, to the final dose estimation model 345, a difference (i.e., Y−Ŷ)between the actual Y in training data 305 and the predicted Ŷ. In thiscase, dose estimation model 345 may be expressed as:

Z _(i)=∝+β₁ X _(i1)+β₂ X _(i2)+γ(Y _(i) −Ŷ _(i))ε_(i).

In a simple example shown in FIG. 3, assuming ∝=0, β₁=2.3, β₂=−0.7,γ=3.5 and {circumflex over (γ)}=0.95 (e.g., average value of Y_(i) inthe training data) are calculated based on training data 305, doseestimation model 345 may be expressed using the equation below. Notethat parameters x₁=rectum volume; x₂=closest distance between rectum andprostate; y=relative target volume covered by a dose prescription level;and z=mean dose on rectum.

z=2.3x ₁−0.7x ₂+3.5(y−0.95).

The above model allows a clinician to take into account the treatmentplanning trade-off between better target coverage (modelled using y) andhigher mean dose in rectum (modelled using z). For example, if rectumsparing is preferred, y may be set to a value that is lower than ŷ=0.95(i.e., y<0.95), which results in a lower value of z. Otherwise, ifbetter target coverage is preferred, y may be set to a value that ishigher than ŷ=0.95 (i.e., y>0.95), which results in a higher value of z.

By comparison, a conventional dose estimation model does not take intoaccount any treatment planning trade-off. Using the same patientgeometrical features x₁ and x₂, the conventional model may berepresented as follows:

z ₀ =f ₀(x ₁ , x ₂)=2.3x ₁−0.7x ₂.

In the above conventional model, z₀=f₀(x₁,x₂) may be interpreted as theestimated radiation dose given x₁ and x₂, under the assumption that allother treatment parameters are following the training data distribution.

In contrast, according to examples of the present disclosure, z=f (x₁,x₂, y) may be interpreted as the estimated radiation dose given x₁ andx₂, under the assumption that y deviates from the training datadistribution. In at least some examples, the sum of squared differencesbetween z and Z is expected to be smaller than or equal to the samequantity calculated over z₀, i.e., (z−Z)²≤(z₀−Z)² or (f(x₁, x₂,y)−Z)²≤(f₀(x₁, x₂)−Z)².

Treatment Plan Generation and Treatment Delivery

FIG. 4 is a flowchart of example process 400 for a computer system toautomatically generate a treatment plan based on the dose estimationmodel trained according to example process 300 in FIG. 3. The variousblocks may be combined into fewer blocks, divided into additionalblocks, and/or eliminated based upon the desired implementation. Similarto the example in FIG. 2, example process 200 may be implemented usingany suitable computer system, an example of which will be discussedusing FIG. 9.

At 410 in FIG. 4, a dose estimation model is selected for theradiotherapy treatment planning of a particular patient. In practice,example process 300 in FIG. 3 may be repeated to determine multiple doseestimation models 405 from a single set of training data 305, ormultiple sets of training data 305. The dose estimation may be selectedbased on any suitable factor(s), such as a treatment region of thepatient, a treatment planning trade-off preferred by the clinician, etc.

At 420 and 430 in FIG. 4, first input data x_(in) relating to patientgeometry of the particular patient, and second input data y_(in)relating to treatment planning trade-off are obtained. In practice,first input data x_(in) and second input data y_(in) may be “obtained”using any suitable approach, such as received via a GUI provided by thecomputer system, retrieved from storage, derived from other inputs(e.g., “instruction” discussed below), etc. Using the example in FIG. 3,x_(in) is a 2D vector (x₁, x₂) and y_(in)=y, where x₁=rectum volume ofthe patient; x₂=closest distance between the patient's rectum andprostate; and y=relative target volume receiving at least apredetermined level of radiation.

In practice, second input data y_(in) may be in any suitable form, suchas a continuous value in relative terms to Y in training data 305, astandard deviation of the distribution of Y, etc. Second input datay_(in) may also be in a “user-friendly” form, such as a discreteclassification instruction. In one example, the instruction may be‘emphasize on rectum sparing,’ which is then used to derive or determinea quantitative value. For example, using dose estimation modelz=2.3x₁−0.7x₂+3.5(y−0.95), rectum sparing may be emphasized by selectinga lower target coverage y<0.95 (e.g., less than 95% receiving at least45 Gy) to obtain a lower value for z=mean rectum dose. On the otherhand, instruction=‘emphasize on target coverage’ will produce theopposite effect.

At 440 in FIG. 4, first input data x_(in) relating to patient geometryof the new patient, and second input data y_(in) relating to treatmentplanning trade-off are transformed into output data z_(out) using doseestimation model 345. As discussed using FIG. 3, output data z_(out) mayexpress radiation dose in any suitable form, such as DVH, dosedistribution, etc. A treatment plan is generated based on output dataz_(out) for treatment delivery using any suitable radiotherapy treatmentsystem (see FIG. 5). In practice, estimates produced by dose estimationmodel 345 may be optimized according to other objectives set by theclinician to produce complete treatment plan.

FIG. 5 is a schematic diagram of example radiotherapy treatment system500 for treatment delivery according to a treatment plan generatedaccording to example process 400 in FIG. 4. Although an example is shownin FIG. 5, it will be appreciated any alternative and/or additionalconfiguration may be used depending on the desired implementation.Radiotherapy treatment system 500 includes radiation source 510 toproject radiation beam 520 onto treatment volume 140 (see also FIG. 1)representing the patient's anatomy at various beam angles 530.

Although not shown in FIG. 5 for simplicity, radiation source 510 mayinclude a linear accelerator to accelerate radiation beam 520 and acollimator (e.g., multileaf collimator (MLC)) to modify or modulateradiation beam 520. In another example, radiation beam 520 may bemodulated by scanning it across a target patient in a specific patternwith various dwell times (e.g., as in proton therapy). A controller(e.g., computer system) may be used to control the operation ofradiation source 520 according to treatment plan 160.

During treatment delivery, radiation source 510 may be rotatable (e.g.,using a gantry) around a patient, or the patient may be rotated (as insome proton radiotherapy solutions) to emit radiation beam 520 atvarious beam angles 530 relative to the patient. For example, fiveequally-spaced beam angles 530 (e.g., angle α=“a”, “b”, “c”, “d” and“e”; also known as “fields”) may be selected for radiation source 510.In practice, any suitable number of beam and/or table or chair angles530 (e.g., five, seven, nine, etc.) may be selected. At each beam angle530, radiation beam 520 is associated with fluence plane 540 (also knownas an intersection plane) situated outside the patient envelope along abeam axis extending from radiation source 510 to treatment volume 140.As shown, fluence plane 540 is generally at a known distance from theisocenter.

In addition to beam angles 530, fluence parameters of each radiationbeam 520 are required for treatment delivery. The term “fluenceparameters” may refer generally to characteristics of radiation beam520, such as its intensity profile as represented using fluence maps(e.g., 550 a, 550 b, 550 c, 550 d and 550 e for corresponding beamangles 530 “a”, “b”, “c”, “d” and “e”). Each fluence map (e.g., 550a)represents the intensity of radiation beam 520 at each point on fluenceplane 540 at a particular beam angle 530 (e.g., “a”). Treatment deliverymay then be performed according to fluence maps 550, such as using IMRT,etc. The radiation dose deposited according to fluence maps 550 should,as much as possible, correspond to the treatment plan generated usingdose estimation model 345 according to examples of the presentdisclosure. In practice, fluence maps 550 may be optimized, such asbased on the physical characteristics of radiotherapy treatment system500.

Improvements

In the following, approaches for improving the dose estimation model(e.g., DVH estimation model), and particularly the training data, willbe discussed. The approaches below in FIG. 6 to FIG. 8 may be usedindependently from, or together with, the examples described using FIG.1 to FIG. 5.

As discussed above, a dose estimation model may be generated based ontraining data that includes treatment plans associated with pastpatients. In order to generate a dose estimation model that may bestspare the OARs without compromising the target, it is important that thetraining data is as “optimal” as possible. According to examples of thepresent disclosure, radiotherapy treatment planning may be improved byidentifying sub-optimal characteristics associated with the trainingdata. Based on the sub-optimal characteristics, the training data may beimproved to generate an improved dose estimation model.

In more detail, FIG. 6 is a flowchart of example process 600 for acomputer system to perform radiotherapy treatment planning. Exampleprocess 600 may include one or more operations, functions, or actionsillustrated by one or more blocks, such as 610 to 670. The variousblocks may be combined into fewer blocks, divided into additionalblocks, and/or eliminated based upon the desired implementation. Theexamples below may be used for any suitable radiotherapy treatmentplanning, such as relating to cancer treatment, etc. For example, inrelation to lung cancer, target 146 represents cancerous lung tissue,and OAR 148 may be proximal healthy lung tissue, esophagus, heart, etc.In relation to prostate cancer, target 146 represents a patient'sprostate, and OAR 148 a proximal healthy structure such as rectum,bladder, etc. In the following, various examples will be described inrelation to prostate cancer.

At 610 in FIG. 6, training data that includes multiple treatment plansassociated with respective multiple past patients are obtained. The term“obtain” may refer generally to retrieving the training data from anysuitable storage (e.g., database of historical treatment plans)accessible by the computer system, receiving the training data fromanother source via any suitable communication link, etc.

At 620, 630 and 640 in FIG. 6, the training data is processed todetermine, from each of the multiple treatment plans, at least one ofthe following: first data associated with a particular past patient or aradiotherapy system for delivering radiotherapy treatment to theparticular past patient (see 620), second data associated with treatmentplanning trade-off selected for the particular past patient (see 630)and third data (see 640) associated with radiation dose for delivery tothe particular past patient.

At 650 in FIG. 6, based on at least one of the first data, the seconddata and the third data, one or more sub-optimal characteristicsassociated with training data are identified for use in improving thetraining data to generate a dose estimation model. Here, the term“sub-optimal characteristic” may refer to any suitable characteristicassociated with one or more treatment plans that may be reduced, oreliminated, to improve the optimality or quality of the training data.This way, the training data may be improved based on the sub-optimalcharacteristic(s) to improve the resulting dose estimation model.

For example, at 660 in FIG. 6, improved training data may be obtained.The improved training data may be generated to reduce or eliminate theidentified sub-optimal characteristic(s) associated with the trainingdata. If multiple sub-optimal characteristics are identified at block650, the improved training data may reduce or eliminate at least onesub-optimal characteristic. Similar to block 610, the term “obtain” mayrefer generally to the computer system retrieving the improved trainingdata from any suitable storage, receiving the improved training datafrom another source via any suitable communication link, etc. Dependingon the desired implementation, the improved training data may begenerated manually by a user (e.g., clinician) or automatically by thecomputer system, such as by improving at least one of the treatmentplans in the training data, modifying the training data by includingadditional and/or alternative treatment plan(s) in the training data,removing treatment plan(s) from the training data, etc.

At 670 in FIG. 6, a dose estimation model may be generated using theimproved training data. Depending on the desired implementation, blocks660 and 670 may be performed independently from blocks 610 to 650 usingthe same computer system, or a different computer system. In the lattercase, the term “computer system” refers to a group of multiple computersystems configured to perform blocks 610 to 670. Improvement to thetraining data may be made by a user (e.g., clinician) or automaticallyusing a computer system.

As will be discussed further using FIG. 7, sub-optimality in the form oflack of training data coverage may be identified using the example inFIG. 6. In this case, a first sub-optimal characteristic in the form ofa gap associated with the first data (X), the second data (Y), or acombination of the first data and the second data (X, Y) may beidentified at block 650. The gap may be identified based on multipledata clusters associated with the first data, the second data, or thecombination of the first data and the second data. In this case,improved training data that includes treatment plan(s) that reduce oreliminate the gap may be obtained at block 660 to generate the doseestimation model at block 670.

As will be discussed further using FIG. 8, sub-optimal treatment plansmay be identified using the example in FIG. 6. In this case, a secondsub-optimal characteristic in the form of a particular treatment planthat requires re-planning, or a particular treatment plan that includesa treatment planning trade-off, may be identified at block 650. Here,when a “trade-off” is referred to as a sub-optimal characteristic, itshould be noted that the trade-off generally cannot be modelled orexplained using the second data (Y) discussed above. As such, suchsub-optimal treatment plans are usually considered as “outliers” thatmay adversely affect the optimality of the resulting dose estimationmodel, and the identification of the outliers facilitates improvement tothe training data. In this case, improved training data that includestreatment plan(s) after the re-planning has been performed may beobtained at block 660 to generate the dose estimation model at block670. Depending on the dose estimation model, treatment plan(s) thatinclude planning trade-off may be eliminated from the training data totrain a dose estimation model without planning trade-off. Alternatively,the treatment plan(s) may be retained as part of the training data totrain a dose estimation model with planning trade-off.

As discussed above, the improved training data at block 660 may begenerated manually or automatically by the computer system. For example,based on the first sub-optimal characteristic, the computer system maygenerate the improved training data by including at least onealternative or additional treatment plan in the training data to reduceor eliminate the gap. Additionally or alternatively, based on the secondsub-optimal characteristic, the computer system may generate theimproved training data by performing the required re-planning on aparticular treatment plan. After the re-planning is performed. theparticular treatment plan will be included in the training data.

Similar to the examples in FIG. 1 and FIG. 5, the term “process” or“processing” at blocks 620 to 640 may include any suitable dataprocessing operation(s), such as data analysis, feature extraction,calculation, derivation, transformation, any combination thereof, etc.Also, any suitable algorithm may be used to estimate the dose estimationmodel at block 670, such as regression algorithm (e.g., stepwisemultiple regression, linear regression, polynomial regression, etc.),principal component analysis (PCA) algorithm, classification algorithm,clustering algorithm, machine learning algorithm (e.g., supervisedlearning, unsupervised learning), etc.

(a) Identifying gap(s) in Training Data in a Dose Estimation Model

The estimation capability of a dose estimation model often depends onthe coverage of the training data. For example, in relation to patientgeometry data coverage, a good coverage may lead to, in general, betterestimated results for all the anatomical cases that are included in thedata range. On the other hand, poor patient geometry data coverage maylead to poorer estimation results for the anatomical cases that fall inthe “gap” of missing data.

Unfortunately, the process of generating a dose estimation model isiterative. To generate a dose estimation model, a user (e.g., clinician)often needs to add training cases, analyze and/or verify results of thedose estimation model, add more training cases if necessary (sometimesblindly), and update the dose estimation model. This iterative processis tedious and time-consuming. In some cases, the additional trainingcases will not help to improve the model results because they arealready covered by existing training cases.

To improve the above process, the training data may be automaticallyanalyzed to identify “gaps” or “missing cases” in the training data.This knowledge may then be used to reduce the number of iterationsrequired to improve the results of the dose estimation model. Inrelation to the patient geometry data, the gaps may be calculated basedon a single individual anatomical feature (i.e., 1 D-gaps), such astarget volume, organ-at-risk (OAR) volume, relative overlap volume,relative out-of-field volume, etc. Multi-dimensional gaps in patientgeometry data may also be identified based on a combination ofanatomical features.

There are multiple ways to identify the gaps in any number ofdimensions. In one example, a clustering algorithm may be used toidentify where the data is clustered, and where the data is lacking bymeasuring cluster's center and radius in the multiple dimensions. Thedistance between boundaries of different clusters may then be calculatedto identify the gap as, for example, a distance between clusters that ishigher than a certain threshold. The threshold may be different for eachdimension, or a combination of dimensions. The threshold may also bedependent on the amount of data available or constant. In anotherexample, a simpler approach may be used to determine the gaps in asingle dimensionality setting by dividing the data range into multipleparts (e.g., equal parts) and verifying that each part contains at leastone data point.

The above approach may be used to improve a dose estimation model in theform of Z=f1(X), where Xis a set of feature(s) associated with patientanatomy, Z is a set of feature(s) associated with the estimatedradiation dose. Alternatively or additionally, the above approach may beused to improve a dose estimation model in the form of Z=f2(X, Y). Asdiscussed using FIG. 1 to FIG. 5, parameter Y represents a set offeature(s) associated with treatment planning trade-off. In this case,gaps in parameter Y may be identified to improve the coverage oftreatment planning trade-off data. Similarly, the gaps may be one- ormulti-dimensional.

Gaps may also be identified in, not only the patient geometry data andtrade-off data, but also in other parameters that affect the doseestimation model, such as number of fields, directionality of the fields(angles), etc.

In more detail, FIG. 7 is a schematic diagram illustrating exampleprocess 700 for a computer system to identify a first examplesub-optimal characteristic in the form of gap(s) in training data.Example process 700 may include one or more operations, functions,actions or data items illustrated by one or more blocks, such as 705 to796. The various blocks may be combined into fewer blocks, divided intoadditional blocks, and/or eliminated based upon the desiredimplementation.

Referring first to 705 in FIG. 7, first training data that includes Ntreatment plans associated with respective multiple past patients isobtained, such as by retrieving from a database of past treatment plans,receiving from another computer system via a communication link, etc.First training data 705 may be obtained based on their relevance to aparticular treatment area, such as rectum in the example below. Aparticular treatment plan in first training data 705 may be denoted asthe i^(th) treatment plan, where i=1, . . . , N.

At 710 in FIG. 7, first training data 705 is processed to determinefirst data 715 required to train a dose estimation model. First data 715may be represented using a N×K matrix denoted as X. The feature(s) fromthe i^(th) treatment plan may be represented as X_(i)=(X_(i1), . . . ,X_(iK)), i=1, . . . , N. In practice, first data 715 may include anysuitable patient geometrical or anatomical feature(s) that can beextracted or derived from first training data 705, such as targetvolume; OAR volume; relative volume; relative out-of-field volume, etc.Distance to target histograms (DTHs) that measure the distance fromparticular target 146 may also be derived from training data 705.Additionally or alternatively, first data 715 may includenon-geometrical features relating to the radiotherapy treatment system,such as number of fields in the treatment plan, directionality of thefields, etc. First data 715 may also include other non-geometricalfeatures, such as a prescription given to the patient (e.g.,fractionation schema), used photon energy, etc.

At 720 in FIG. 7, clustering is performed to generate multiple dataclusters 725 associated with first data 715. As used herein, the term“clustering” may refer generally to the partitioning of first data 715into multiple subsets, each subset representing a data cluster. Forexample, one approach is to partition with X into multiple data clustersthat represent equal partitions of the data range of first data 715. Inanother example, a clustering algorithm may be used to generate dataclusters 725, such as density-based spatial clustering, k-meansclustering, hierarchical clustering, etc.

For example, density-based spatial clustering may be performed tocategorize X_(i)=(X_(i1), . . . , X_(iK)) associated with the i^(th)treatment plan into one of the following: (i) core case that has apredetermined number of neighbors within their vicinity, (ii) bordercase that has fewer neighbors that the predetermined number but lies ina neighborhood of at least one core case, and (iii) noise case that isneither a core case nor border case. Depending on the desiredimplementation, two core cases may be assigned to the same cluster ifone is a neighbor of the other. In this case, the neighbors of the corecases are also assigned to that same cluster.

At 730 in FIG. 7, data clusters 725 are analyzed to detect at least onegap 735 associated with first data 715. For example, a “gap” may bedefined as a distance between two clusters that exceeds a predeterminedthreshold in a feature space of first data 715, such as a certainpercentage (e.g., 10%) of the total variation in first data 715. In thecase of prostate cancer treatment planning, X_(i) may includegeometrical features such as bladder volume, bladder mean distance totarget, etc. In this case, the gap may represent insufficient datacoverage for a certain range of values associated with bladder volume orbladder mean distance to target, or a combination thereof.

At 740 in FIG. 7, improved first data (X′) 745 that reduces oreliminates gap 735 associated with first data (X) 715 may be obtained.Compared to first data (X) 715 from (original) training data 705,improved first data (X′) 745 may be identified from improved trainingdata that includes N′ treatment plans. In particular, to reduce oreliminate gap 735, additional and/or alternative treatment plans may beselected by a user (e.g., clinician) manually or by a computer systemautomatically. Feature(s) from the i^(th) treatment plan may berepresented as X′_(i)(X′_(i1), . . . , X′_(iK)), i=1, . . . , N′.

At 750 in FIG. 7, dose estimation model 755 is trained using improvedfirst data (X′) 745 to estimate a relationship that transformsindependent data (X′) to dependent data Z′. For example, therelationship or interdependency may be expressed using any suitablefunction f1( ) as follows:

Z′=f1(X′).

Dependent data Z′ may include M≥1 feature(s) associated with radiationdose to be delivered, and represented using a N′×M matrix. Feature(s)from the i^(th) treatment plan may be represented using Z′_(i)=(Z′_(i1),. . . , Z′_(iM)), i=1, . . . , N′. In practice, radiation dose may bespecified using dose distribution, DVH, etc.

Depending on the desired implementation, example process 700 may beimplemented to train a dose estimation model with treatment planningtrade-off(s). In this case, at 760 in FIG. 7, training data 705 isprocessed to determine second data 765 that includes L≥1 feature(s)associated with treatment planning trade-off. Second data 765 may berepresented using a N×L matrix denoted as Y, and feature(s) from thei^(th) treatment plan may be represented as Y_(i) =(Y_(i1), . . . ,Y_(iL)), i=1, . . . , N. For example, in the case of L=1, Y_(i) is a 1Dvector with a singular value Y_(i).

In practice, the treatment planning trade-off in second data 765 may bebetween a first objective and a second objective, such as a one-to-onetrade-off may be between a first objective associated with OAR 148(e.g., rectum) and a second objective associated with target 146 (e.g.,prostate). In this case, the first objective may relate to dose sparingof OAR 148, and the second objective may relate to better targetcoverage or better target dose conformity. In this example, the seconddata may include one or more dosimetrical features, such as relativetarget volume with a particular prescribed dose or higher, desirednormalization volume for 98% dose level (or any other suitable level),etc. Dose sparing of OAR 148 may be characterized using OAR dose level,such as mean dose, median dose, maximum dose, minimum dose, etc. Variousexamples of “first objective” and “second objective” have been discussedusing FIG. 3 and will not be repeated here for brevity.

At 770 in FIG. 7, clustering is performed to generate multiple dataclusters 775 associated with second data 765 (Y), or a combination offirst data 745 and second data 765 (X, Y). At 780 in FIG. 7, based ondata clusters 775, at least one gap 785 associated with second data 765(Y), or a combination of first data 745 and second data 765 (X, Y), isidentified. For example, in relation to prostate cancer, the doseestimation model may be designed to take into account the treatmentplanning trade-off between higher mean dose in rectum (i.e., firstobjective) and better target coverage (i.e., second objective). In afirst example with L=1, clustering may be performed to generate 1D dataclusters 775 (i.e., partitions) associated with second data 765 (Y).

In another example, consider an example with K=2 and L=1. In this case,first data 715 and second data 765 may be expressed as (X_(i),Y_(i))=X_(i1), X_(i2), Y_(i1)), where X_(i1)=bladder volume,X_(i2)=bladder mean distance to rectum, Y_(i)=relative target volumecovered by a dose prescription level and i=1, . . . , N represents thei^(th) treatment plan. Using a clustering algorithm such asdensity-based spatial clustering, 3D data clusters 775 by categorizing(X_(i), Y_(i)) into one of the following: (i) core case, (ii) bordercase and (iii) noise case. Two core cases may be assigned to the samecluster if one is a neighbor of the other. A 3D gap between two dataclusters 775 may be identified in response to determination that a 3Dspace between them exceeds a predetermined threshold.

At 790 in FIG. 7, improved data (Y′) or (X′, Y′) 795 that reduces oreliminates identified gap 785 associated with corresponding (Y) or (X,Y) may be obtained. Compared to (Y) or (X, Y) from (original) trainingdata 705, (Y′) or (X′, Y′) may be identified from improved training datathat includes any additional and/or alternative treatment plans thatreduce or eliminate identified gap 785. The improved training data maybe generated based on identified gap 785 by a user (e.g., clinician)manually or by a computer system automatically. Depending on the desiredimplementation, the computer system may automatically select additionaland/or alternative treatment plan(s) to reduce or eliminate identifiedgap 785, and recommend the treatment plan(s) to a user via a userinterface.

At 796 in FIG. 7, dose estimation model 797 is trained using improveddata (Y′) or (X′, Y′) 795 to estimate a relationship that transformsindependent data (X′, Y′) to dependent data Z′. For example, therelationship or interdependency may be expressed using any suitablefunction f2( ) as follows:

Z′=f2(X′,Y′).

Dependent data Z′ may include M≥1 feature(s) associated with radiationdose to be delivered, and represented using a N″×M matrix. Feature(s)from the i^(th) treatment plan may be represented using Z′_(i)=(Z′_(i1),. . . , Z′_(iM)), i=1, . . . , N″. In practice, radiation dose may bespecified using dose distribution, DVH, etc.

(b) Identifying Candidate Plans for Re-Planning and Trade-Off PlansUsing a Dose Estimation Model

Conventionally, there is no automatic way to guide a user about“non-optimal” training cases in the input training data. For example,the optimality of a plan may be interpreted as achieving the best OARsparing possible without compromising the target. A user could try toimprove the optimality of the dose estimation model using an iterativeprocess, which includes building the model and re-planning the set oftraining cases with the current model. However, re-planning all thetraining cases is time-consuming and inefficient.

To improve the process of generating the dose estimation model,“non-optimal” plans that possibly require a re-plan may be identified,such as after every iteration of model training. In one example, suchplans may be identified by comparing the lower and upper DVH estimatemean doses with respect to the clinical DVH mean dose for each OAR andtarget in a plan. Based on the comparison, the following scenarios maybe observed.

In one scenario, if the mean doses for all the OARs fall within theupper and lower estimate mean doses as well as the target mean dose, theplan may be considered of standard quality given the current doseestimation model. In other scenarios, the plan may be worse thanexpected (indicating a possible re-plan), better than expected, or amix. In another scenario, if all the OARs have higher mean dose than thelower estimate mean dose, and some have higher mean dose than the upperestimate mean dose and/or the target mean dose is higher than the lowerestimate mean dose, it is considered that the plan is a candidate forre-planning (i.e., the plan is worse than expected). In a furtherscenario, if some OARs mean doses are below the lower mean dose estimatewhile others are above, the plan is considered to have trade-offs.

Threshold(s) or margin(s) may be set to identify plans that mostsignificantly require re-planning or contain trade-offs more accurately.By identifying plans that require re-planning, the user is guided tofocus on what to improve when building the dose estimation model. Thisway, the user may make more conscious decisions about what to include orexclude from the training set.

In more detail, FIG. 8 is a schematic diagram illustrating exampleprocess 800 for a computer system to identify a second examplesub-optimal characteristic in the form of treatment plan(s) that requirere-planning or include planning trade-off in training data. Exampleprocess 800 may include one or more operations, functions, actions ordata items illustrated by one or more blocks, such as 805 to 875. Thevarious blocks may be combined into fewer blocks, divided intoadditional blocks, and/or eliminated based upon the desiredimplementation.

Referring first to 805 in FIG. 8, first training data that includes Ntreatment plans associated with respective multiple past patients isobtained, such as by retrieving from a database of past treatment plans,receiving from another computer system via a communication link, etc.First training data 805 may be obtained based on their relevance to aparticular treatment area, such as rectum in the example below.

At 810, 820 and 830 in FIG. 8, training data 805 is processed todetermine respective first data (X) 815, second data (Y) 825 and thirddata (Z) 835. Similar to the example in FIG. 3, first data 815, seconddata 825 and third data 835 from the i^(th) treatment plan may berepresented as X_(i)=(X_(i1), . . . , X_(iK)), Y_(i)=(Y_(i1), . . . ,Y_(iL)) and Z_(i)=(Z_(i1), . . . , Z_(iM)), where i=1, . . . , N.

At 840 in FIG. 8, dose estimation model 845 is estimated. Depending onthe desired implementation, first data (X) 815 and third data (Z) 835may be used to estimate a dose estimation model f1( ) that transformsindependent data (X) to dependent data Z. Alternatively, first data (X)815, second data (Y) 825 and third data (Z) 835 may be used to estimatea dose estimation model with treatment planning trade-off(s) f2( ) thattransforms independent data (X, Y) to dependent data Z.

At 850 in FIG. 8, based on third data (Z) 835 and the dose estimationmodel, each i^(th) treatment plan in training data 805 may be identifiedas one of the following: (a) treatment plan that is of standard quality(see 852), (b) treatment plan that requires re-planning (see 854) and(c) treatment plan with trade-off (see 856). In relation to (c), itshould be noted that the “trade-off” may be a sub-optimal characteristicthat that cannot be modelled or explained using second data (Y) 825.

In practice, block 850 may be based on a comparison between comparing Z,from the i^(th) treatment plan with lower radiation dose data and upperradiation dose data estimated using dose estimation model 845. Forexample, Z_(i) may include mean radiation dose data associated withmultiple OARs. For example, a particular treatment plan may be acandidate for re-planning in response to determination that the meanradiation dose data is higher than the estimated lower radiation dosedata for all of the multiple OARs, and higher than the estimated upperradiation dose data for some of the multiple OARs. In other words, allquality metrics are worse or equal than predicted. In relation toprostate cancer, this may occur when the target (i.e., prostate)coverage is worse than dose estimation model 845 predicts, but at thesame time the mean dose for OARs (e.g., rectum, bladder, etc.) is higherthan expected.

In another example, a particular treatment plan may include a treatmentplanning trade-off in response to determination that the mean radiationdose data is lower than the estimated lower radiation dose data for someof the multiple OARs, and lower than the estimated upper radiation dosedata for some of the multiple OARs. In this case, some of the qualitymetrics are significantly better than predicted by the dose estimationmodel, while some other quality metrics are significantly worse. Forexample, the target coverage for an individual case may better than whatdose estimation model 845 predicts, while the rectum dose is higher. Inthis case, it is difficult to say whether the plan is better or worse—itmerely demonstrates that possibly the planner has had different planningpriorities in general.

At 860 in FIG. 8, re-planning is performed on treatment plan(s) 854 togenerate improved training data 865. In practice, the re-planning may bepreferred over simply removing treatment plan(s) 854 in variousscenarios, such as when the number of treatment plans available for astraining data is a limiting factor, etc. The re-planning may beperformed by a user (e.g., clinician), or automatically using a computersystem. In practice, the user may perform the re-planning manually byrepeating the original optimization process but not knowing that theprevious treatment plan was labelled as sub-optimal. (Note that theoriginal treatment plan might have been created by less experiencedplanner or due to time pressure). Automatic re-planning may be performedbased on dose estimation model 845 (e.g., Z=f2(X,Y)) to improve features(e.g., mean radiation dose data) that are identified to be sub-optimal.For example, once re-planning is performed, the mean radiation dose dataof treatment plan(s) 854 may be within an acceptable range, such asbetween the estimated lower radiation dose data and estimated upperradiation dose data for all OARs. This should be contrasted against theconventional approach of users blindly re-planning treatment plans.

At 870 in FIG. 8, improved dose estimation model 875 is generated basedon improved training data 865 that includes treatment plan(s) 845 afterre-planning is performed. In one example, improved training data 865 mayinclude treatment plans 852 that are determined to be of standardquality 852 to train a dose estimation model Z′=f3(X′) by estimating arelationship that transforms independent data (X′) to dependent data Z′.Alternatively or additionally, improved training data 865 may includetreatment plans that are of standard quality 852, treatment plan(s) 845that are re-planned, and treatment plans that have trade-off(s) 856 totrain a dose estimation model Z′=f4(X′, Y′) by estimating a relationshipthat transforms independent data (X′, Y′) to dependent data Z′.

Once improved dose estimation model 875 is generated, it may be used forradiotherapy treatment planning, such as according to blocks 410-420 and440 for Z′=f3(X′) or blocks 410-440 for Z′=f4(X′, Y′) according to theexamples in FIG. 4. Treatment delivery according to a treatment plan maybe performed according to the examples in FIG. 5.

In practice, multiple iterations of the example process in FIG. 7 may berequired to generate improved dose estimation model 875. At eachiteration, sub-optimal characteristics in the form of treatment plansthat require re-planning may be identified from the training data. Afterthe re-planning is performed and the dose estimation model updated,additional treatment plans that require re-planning may be identifiedbecause changes to the training data also changes the dose estimationmodel. Any suitable algorithm may be used to generate dose estimationmodel 845/875, such as regression algorithm (e.g., stepwise multipleregression, linear regression, polynomial regression, etc.), principalcomponent analysis (PCA) algorithm, classification algorithm, clusteringalgorithm, machine learning algorithm (e.g., supervised learning,unsupervised learning), etc.

Computer System

The above examples can be implemented by hardware, software or firmwareor a combination thereof. FIG. 9 is a schematic diagram of examplecomputer system 900 for radiotherapy treatment planning and exampleradiotherapy treatment system 500 for treatment delivery. In thisexample, computer system 905 (also known as a treatment planning system)may include processor 910, computer-readable storage medium 920,interface 940 to interface with radiotherapy treatment system 500, andbus 930 that facilitates communication among these illustratedcomponents and other components.

Processor 910 is to perform processes described herein with reference toFIG. 1 to FIG. 8. Computer-readable storage medium 920 may store anysuitable information 922, such as information relating to training data305/705/805, first data 315/715/815, second data 325/765/825, third data335/835, dose estimation model 345/755/797/875, etc. Computer-readablestorage medium 920 may further store computer-readable instructions 624which, in response to execution by processor 910, cause processor 910 toperform processes described herein.

As explained with reference to FIG. 5, example radiotherapy treatmentsystem 500 may include rotatable gantry 950 to which radiation source510 is attached. During treatment delivery, gantry 950 is rotated aroundpatient 970 supported on structure 680 to emit radiation beam 520 atvarious beam angles 530, as explained with reference to FIG. 5.Radiotherapy treatment system 500 may further include controller 960 toobtain (e.g., receive, retrieve from storage, etc.) treatment plan 160(see also FIG. 1) devised by computer system 905 to control gantry 970,radiation source 510 and radiation beam 520 to deliver radiotherapytreatment.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof.

Those skilled in the art will recognize that some aspects of theembodiments disclosed herein, in whole or in part, can be equivalentlyimplemented in integrated circuits, as one or more computer programsrunning on one or more computers (e.g., as one or more programs runningon one or more computer systems), as one or more programs running on oneor more processors (e.g., as one or more programs running on one or moremicroprocessors), as firmware, or as virtually any combination thereof,and that designing the circuitry and/or writing the code for thesoftware and or firmware would be well within the skill of one of skillin the art in light of this disclosure.

We claim:
 1. A method for a computer system to perform radiotherapytreatment planning, wherein the method comprises: obtaining trainingdata that includes multiple treatment plans associated with respectivemultiple past patients; processing the training data to determine, fromeach of the multiple treatment plans, at least one of the following:first data associated with a particular past patient or a radiotherapysystem for delivering radiotherapy treatment to the particular pastpatient, second data associated with treatment planning trade-offselected for the particular past patient and third data associated withradiation dose for delivery to the particular past patient; based on atleast one of the first data, the second data and the third data,identifying one or more sub-optimal characteristics associated with thetraining data; obtaining improved training data that is generated toreduce or eliminate the identified one or more sub-optimalcharacteristics associated with the training data; and generating a doseestimation model based on the improved training data.
 2. The method ofclaim 1, wherein identifying the one or more sub-optimal characteristicscomprises one or more of the following: identifying a first sub-optimalcharacteristic in the form of a gap associated with the first data, thesecond data, or a combination of the first data and the second data; andidentifying a second sub-optimal characteristic in the form of aparticular treatment plan that requires a re-planning, or includes atreatment planning trade-off.
 3. The method of claim 2, whereinidentifying the first sub-optimal characteristic comprises: identifyingthe gap based on multiple data clusters associated with the first data,the second data, or the combination of the first data and the seconddata.
 4. The method of claim 2, wherein identifying the secondsub-optimal characteristic comprises: comparing the third dataidentified from the particular treatment plan with estimated lowerradiation dose data and estimated upper radiation dose data, wherein thethird data includes mean radiation dose data associated with multipleorgans-at-risk (OARs).
 5. The method of claim 4, wherein identifying thesecond sub-optimal characteristic comprises one of the following: inresponse to determination that the mean radiation dose data is higherthan the estimated lower radiation dose data for all of the multipleOARs, and higher than the estimated upper radiation dose data for someof the multiple OARs, determining that the particular treatment planrequires the re-planning; and in response to determination that the meanradiation dose data is lower than the estimated lower radiation dosedata for some of the multiple OARs, and lower than the estimated upperradiation dose data for some of the multiple OARs, determining that theparticular treatment plan includes the treatment planning trade-off. 6.The method of claim 2, wherein the method further comprises one or moreof the following: based on the first sub-optimal characteristic,generating the improved training data by including at least onealternative or additional treatment plan in the training data to reduceor eliminate the gap; and based on the second sub-optimalcharacteristic, generating the improved training data by performing therequired re-planning on the particular treatment plan.
 7. The method ofclaim 1, wherein processing the training data comprises one or more ofthe following: determining, from each of the multiple treatment plans,the first data that includes one or more of the following geometricalfeatures associated with the particular past patient: target volume, OARvolume, relative overlap volume and relative out-of-field volume;determining, from each of the multiple treatment plans, the first datathat includes one or more of the following non-geometrical features:number of fields, directionality of the fields, prescription to theparticular past patient and photon energy; determining, from each of themultiple treatment plans, the third data that includes one or more ofthe following features associated with radiation dose: dose volumehistogram (DVH) and dose distribution; and determining, from each of themultiple treatment plans, the second data that is associated with one ofthe following treatment planning trade-offs: trade-off between a firstobjective associated with an organ-at-risk (OAR) and a second objectiveassociated with a target; trade-off between a first objective associatedwith a first OAR and a second objective associated with a second OAR;trade-off between a first objective associated with a target and asecond objective associated with multiple OARs; trade-off between afirst objective associated with a first feature that is non-dosimetricaland a second objective associated with one or more second features; andtrade-off between a first objective associated with a first group offeatures and a second objective associated with a second group offeatures.
 8. A non-transitory computer-readable storage medium thatincludes a set of instructions which, in response to execution by aprocessor of a computer system, cause the processor to perform a methodof radiotherapy treatment planning, the method comprising: obtainingtraining data that includes multiple treatment plans associated withrespective multiple past patients; processing the training data todetermine, from each of the multiple treatment plans, at least one ofthe following: first data associated with a particular past patient or aradiotherapy system for delivering radiotherapy treatment to theparticular past patient, second data associated with treatment planningtrade-off selected for the particular past patient and third dataassociated with radiation dose for delivery to the particular pastpatient; based on at least one of the first data, the second data andthe third data, identifying one or more sub-optimal characteristicsassociated with the training data; obtaining improved training data thatis generated to reduce or eliminate the identified one or moresub-optimal characteristics associated with the training data; andgenerating a dose estimation model based on the improved training data.9. The non-transitory computer-readable storage medium of claim 8,wherein identifying the one or more sub-optimal characteristicscomprises one or more of the following: identifying a first sub-optimalcharacteristic in the form of a gap associated with the first data, thesecond data, or a combination of the first data and the second data; andidentifying a second sub-optimal characteristic in the form of aparticular treatment plan that requires a re-planning, or includes atreatment planning trade-off.
 10. The non-transitory computer-readablestorage medium of claim 9, wherein identifying the first sub-optimalcharacteristic comprises: identifying the gap based on multiple dataclusters associated with the first data, the second data, or thecombination of the first data and the second data.
 11. Thenon-transitory computer-readable storage medium of claim 9, whereinidentifying the second sub-optimal characteristic comprises: comparingthe third data identified from the particular treatment plan withestimated lower radiation dose data and estimated upper radiation dosedata, wherein the third data includes mean radiation dose dataassociated with multiple organs-at-risk (OARs).
 12. The non-transitorycomputer-readable storage medium of claim 11, wherein identifying thesecond sub-optimal characteristic comprises one of the following: inresponse to determination that the mean radiation dose data is higherthan the estimated lower radiation dose data for all of the multipleOARs, and higher than the estimated upper radiation dose data for someof the multiple OARs, determining that the particular treatment planrequires the re-planning; and in response to determination that the meanradiation dose data is lower than the estimated lower radiation dosedata for some of the multiple OARs, and lower than the estimated upperradiation dose data for some of the multiple OARs, determining that theparticular treatment plan includes the treatment planning trade-off. 13.The non-transitory computer-readable storage medium of claim 9, whereinthe method further comprises one or more of the following: based on thefirst sub-optimal characteristic, generating the improved training databy including at least one alternative or additional treatment plan inthe training data to reduce or eliminate the gap; and based on thesecond sub-optimal characteristic, generating the improved training databy performing the required re-planning on the particular treatment plan.14. The non-transitory computer-readable storage medium of claim 8,wherein processing the training data comprises one or more of thefollowing: determining, from each of the multiple treatment plans, thefirst data that includes one or more of the following geometricalfeatures associated with the particular past patient: target volume, OARvolume, relative overlap volume and relative out-of-field volume;determining, from each of the multiple treatment plans, the first datathat includes one or more of the following non-geometrical features:number of fields, directionality of the fields, prescription to theparticular past patient and photon energy; determining, from each of themultiple treatment plans, the third data that includes one or more ofthe following features associated with radiation dose: dose volumehistogram (DVH) and dose distribution; and determining, from each of themultiple treatment plans, the second data that is associated with one ofthe following treatment planning trade-offs: trade-off between a firstobjective associated with an organ-at-risk (OAR) and a second objectiveassociated with a target; trade-off between a first objective associatedwith a first OAR and a second objective associated with a second OAR;trade-off between a first objective associated with a target and asecond objective associated with multiple OARs; trade-off between afirst objective associated with a first feature that is non-dosimetricaland a second objective associated with one or more second features; andtrade-off between a first objective associated with a first group offeatures and a second objective associated with a second group offeatures.
 15. A computer system configured to perform radiotherapytreatment planning, the computer system comprising: a processor and anon-transitory computer-readable medium having stored thereoninstructions that, when executed by the processor, cause the processorto: obtain training data that includes multiple treatment plansassociated with respective multiple past patients; process the trainingdata to determine, from each of the multiple treatment plans, at leastone of the following: first data associated with a particular pastpatient or a radiotherapy system for delivering radiotherapy treatmentto the particular past patient, second data associated with treatmentplanning trade-off selected for the particular past patient and thirddata associated with radiation dose for delivery to the particular pastpatient; based on at least one of the first data, the second data andthe third data, identify one or more sub-optimal characteristicsassociated with the training data; obtain improved training data that isgenerated to reduce or eliminate the identified one or more sub-optimalcharacteristics associated with the training data; and generate a doseestimation model based on the improved training data.
 16. The computersystem of claim 15, wherein the instructions for identifying the one ormore sub-optimal characteristics cause the processor to perform one ormore of the following: identify a first sub-optimal characteristic inthe form of a gap associated with the first data, the second data, or acombination of the first data and the second data; and identify a secondsub-optimal characteristic in the form of a particular treatment planthat requires a re-planning, or includes a treatment planning trade-off.17. The computer system of claim 16, wherein the instructions foridentifying the first sub-optimal characteristic cause the processor to:identify the gap based on multiple data clusters associated with thefirst data, the second data, or the combination of the first data andthe second data.
 18. The computer system of claim 16, wherein theinstructions for identifying the second sub-optimal characteristic causethe processor to: compare the third data identified from the particulartreatment plan with estimated lower radiation dose data and estimatedupper radiation dose data, wherein the third data includes meanradiation dose data associated with multiple organs-at-risk (OARs). 19.The computer system of claim 18, wherein the instructions foridentifying the second sub-optimal characteristic cause the processor toperform one of the following: in response to determination that the meanradiation dose data is higher than the estimated lower radiation dosedata for all of the multiple OARs, and higher than the estimated upperradiation dose data for some of the multiple OARs, determine that theparticular treatment plan requires the re-planning; and in response todetermination that the mean radiation dose data is lower than theestimated lower radiation dose data for some of the multiple OARs, andlower than the estimated upper radiation dose data for some of themultiple OARs, determine that the particular treatment plan includes thetreatment planning trade-off.
 20. The computer system of claim 16,wherein the instructions further cause the processor to perform one ormore of the following: based on the first sub-optimal characteristic,generate the improved training data by including at least onealternative or additional treatment plan in the training data to reduceor eliminate the gap; and based on the second sub-optimalcharacteristic, generate the improved training data by performing therequired re-planning on the particular treatment plan.
 21. The computersystem of claim 15, wherein the instructions for processing the trainingdata cause the processor to perform one or more of the following:determine, from each of the multiple treatment plans, the first datathat includes one or more of the following geometrical featuresassociated with the particular past patient: target volume, OAR volume,relative overlap volume and relative out-of-field volume; determine,from each of the multiple treatment plans, the first data that includesone or more of the following non-geometrical features: number of fields,directionality of the fields, prescription to the particular pastpatient and photon energy; determine, from each of the multipletreatment plans, the third data that includes one or more of thefollowing features associated with radiation dose: dose volume histogram(DVH) and dose distribution; and determine, from each of the multipletreatment plans, the second data that is associated with one of thefollowing treatment planning trade-offs: trade-off between a firstobjective associated with an organ-at-risk (OAR) and a second objectiveassociated with a target; trade-off between a first objective associatedwith a first OAR and a second objective associated with a second OAR;trade-off between a first objective associated with a target and asecond objective associated with multiple OARs; trade-off between afirst objective associated with a first feature that is non-dosimetricaland a second objective associated with one or more second features; andtrade-off between a first objective associated with a first group offeatures and a second objective associated with a second group offeatures.